How to Install and Uninstall r-cran-randomforest Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 25,2024

1. Install "r-cran-randomforest" package

Here is a brief guide to show you how to install r-cran-randomforest on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                r-cran-randomforest
            
                    
    
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2. Uninstall "r-cran-randomforest" package

Learn how to uninstall r-cran-randomforest on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                r-cran-randomforest
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the r-cran-randomforest package on Ubuntu 21.10 (Impish Indri)

Package: r-cran-randomforest
Architecture: amd64
Version: 4.6-14-3
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian R Packages Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 292
Depends: r-base-core (>= 4.0.2-1build1), r-api-4.0, libc6 (>= 2.29)
Suggests: r-cran-rcolorbrewer, r-cran-mass
Filename: pool/universe/r/r-cran-randomforest/r-cran-randomforest_4.6-14-3_amd64.deb
Size: 211416
MD5sum: fe0a904816ecfdef451609b39ce877f1
SHA1: 30a9c3143af93d620b7415303cc9a519e61aefc1
SHA256: 9d6a6a955a5b5f7068372bc9d1c7fdf40224693d0bdcc713ff1107add3569997
SHA512: 780789cebb75184175b8420dab3f40a4bd383cf5d989e671c592e0ae924a2b0dcda18292e37b84390e28cedc5b9ab61a909f301312eb166ccc7b917cac9581d9
Homepage: https://cran.r-project.org/package=randomForest
Description-en: GNU R package implementing the random forest classificator
RandomForest implements Breiman’s random forest algorithm (based on Breiman
and Cutler’s original Fortran code) for classification and regression. It can
also be used in unsupervised mode for assessing proximities among data points.
.
The technique uses multiple decision trees and combines their individual
votes.
Description-md5: 9c426f8863a49b037052e87707ecc6b1